The increasing prevalence of Large Language Models (LMs) in critical
applications highlights the need for controlled language generation strategies
that are not only computationally efficient but that also enjoy performance
guarantees. To achieve this, we use a common model of concept semantics as
linearly represented in an LM's latent space. In particular, we take the view
that natural language generation traces a trajectory in this continuous
semantic space, realized by the language model's hidden activations. This view
permits a control-theoretic treatment of text generation in latent space, in
which we propose a lightweight, gradient-free intervention that dynamically
steers trajectories away from regions corresponding to undesired meanings.
Crucially, we show that this intervention, which we compute in closed form, is
guaranteed (in probability) to steer the output into the allowed region.
Finally, we demonstrate on a toxicity avoidance objective that the intervention
steers language away from undesired content while maintaining text quality.

使用控制性语言生成策略，通过对概念语义的线性表示在潜空间中实现文本生成的量化处理，以确保性能保证，并通过在闭合形式中计算的小型无梯度干预实现动态轨迹偏离不希望的含义区域，从而避免不良内容并保持文本质量。